Slides for my talk for the Cambridge Visualization of Biological Information Meetup held January 2015. I talk about why biology is exciting for visualisation researchers and go through examples where visualisation can help experts in understanding their data.

1. (Designing)
Interactive
Visualisations to
Solve Analytical
Problems (in biology)
CAGATAY TURKAY,
giCentre, City University London

2. Who?
• Lecturer in Applied Data Science @ the giCentre, CUL
• PhD @ VisGroup at Univ. of Bergen, Norway
• Research interests:
– Integrating Computational Tools in Interactive Visual Analysis
Methods
– Perceptually Optimized Visualization
• Methods for several domains:
– Biology, transport, intelligence, neuroscience

3. giCentre (www.giCentre.net)
• 6 academics
• 2 researchers
• 5 PhDs

4. Data supported science
• Data analysis in almost all scientific fields
–Biology, medicine, astronomy, psychology,…
• Data driven science
• Research in several fields
–Visualization
–Data Mining
–Machine Learning
–Statistics

5. Visualization ?
“Computer-based visualization systems
provide visual representations of datasets
designed to help people carry out tasks more
effectively.” [Tamara Munzner, 2014]
“The use of computer-generated, interactive, visual
representations of data to amplify cognition”[Card,
Mackinlay, & Shneiderman 1999]

6. VIS -- a mature field already

7. Biological data + VIS:
A good synergy
.. but why?

8. Why biology is interesting for VIS?
Datasets are large & heterogeneous
Yeast Protein interaction network, Barabási & Oltvai, 2004
Clustering miR expressions
http://gdac.broadinstitute.org/

10. Why biology is interesting for VIS?
Things happen at multiple scales
[ by O’Donoghue et al., 2010]
[Nye, 2008]

11. Why biology is interesting for VIS?
Processes are dynamic (spatio-temporal
complexity)
Neutrophil chasing a bacteria by David Rogers

12. Why biology is interesting for VIS?
• Computational methods are central in analysis
–Uncertainties hinder reliability
–Interpretation is a problem (black-box alg., little
context)
Comprehensive molecular portraits of human breast tumours, TCGA Network, Nature, 2012

13. How can visualisation help?
• Ease of cognition & communication
• Relating multiple aspects
• Compare multiple computational outputs
• Investigate uncertainties
• Seamless integration of computation
and …
• Enable & foster hypothesis generation

14. Forms of visualisation support
VIS as a presentation medium
+
VIS with interaction
+
VIS with integrated computations

15. Visualisation as a
presentation medium

16. Cross-section of Escherichia coli cell, Illustration by David S. Goodsell, the Scripps Research Institute

17. 106 diffusing and reacting molecules in real-time, Muzic et al., 2014

18. NATURE METHODS: POINTS OF VIEW, by Wong et al.
http://blogs.nature.nom/methagora/2013/07/data-visualization-points-of-view.html

19. Why is VIS good here?
• Analysts’ perceptual & cognitive capabilities
• Better interpretation
• Communication

20. Visualisation
with interaction

21. Example: MizBee - Synteny Browser
Meyer et al., MizBee: A Multiscale Synteny Browser, 2009

23. Why is VIS good here?
• Linking multiple aspects
• Interactively varying the focus
• Display multiple-scales concurrently

24. Visualisation with
integrated computati

25. Combine the best of two worlds:
human capabilities and
power
Facilitate the informed use of
computation through interactive
visual methods
(a.k.a. Visual Analytics)

26. Example: StratomeX, Caleydo
http://caleydo.org

27. Patients(samples)
Genes
Candidate Subtype /
Heat Map
Header /
Summary of
whole Stratification
Cancers have subtypes
• different histology
• different molecular alterations
Subtypes are identified
by stratifying datasets, e.g.,
• based on an expression pattern
• a mutation status
• a copy number alteration
• a combination of these
Case: Cancer Subtype Analysis

28. Multiple Stratifications
Many shared Patients
Clustering 1 Clustering 2
Sample Overlaps

29. Dependent PathwaysSlide by Alex Lex

30. Slide by Alex Lex

31. Multiple Stratifications (again)
Many shared Patients
Clustering 1 Clustering 2
Sample Overlaps
GeneOverlaps??

32. Finding distinctive genes
Characterizing cancer subtypes using dual analysis in Caleydo StratomeX, Turkay et al., IEEE CG&A, 2014

33. Finding distinctive genes (ex. BRCA types)
[*] Cancer Genome Atlas Network. (2012). Comprehensive molecular portraits of human breast tumours. Nature, 490(7418), 61-70.
Luminal-A
underexpressed genes
Luminal-A
overexpressed genes
Basal-like
overexpressed
Basal-like
underexpressed

34. Ex: Cavity analysis in molecular simulations
Cavities on molecular surfaces
• Important in ligand binding
• Drug design, etc.
Long molecular simulations
Cavities are dynamic, hard to track
Amino-acids to characterize the
cavity
• hydrophobicity (grey)
• polarity (green)
• positively charged (blue)
• negatively charged (red)
Visual Cavity Analysis in Molecular Simulations
J. Parulek, C. Turkay, N. Reuter, I. Viola. BMC Bioinformatics, 2013.

35. 1. Run the simulation
2. Fit graphs cavities
3. Compute measures
4. Find touching amino-acids
5. Perform visual analysis
Analysis of Proteinase 3

37. A hydrophobic cavity

38. Why is VIS good here?
• Multiple linked data sets – improve interpretation
• Multiple computational results – deal with
uncertainty
• Integrate computation outputs, i.e., clusters, derived
data
• Allows a fast-paced iterative process
• Quick idea prototyping

39. Wrap up !
VIS as a presentation medium
+
VIS with interaction
+
VIS with integrated computations

40. Visualisation is very good to answer
HOW & WHY?
questions ..
- How do these genomes overlap?
- Why is this a cluster?
....

41. Outlook
• Interaction and explorative analysis is key!
• Seamless support from integrated computation, i.e., t-tests
• Visual analysis as an everyday tool for analysts

42. Thanks ! (& more biovis ?)
http://www.biovis.net
#biovis
Paper deadline: February 15, 2015
Data & Design Contests: May 1, 2015
• VisGroup (Helwig Hauser, Julius Parulek & Ivan Viola) and
Nathalie Reuter from University of Bergen
• Caleydo team (Alex Lex, Hanspeter Pfister, Nils Gehlenborg, Marc Streit)